Christophe Chefd'hotel

Non‐contrast‐enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI

Assessment of regional lung perfusion and ventilation has significant clinical value for the diagnosis and follow‐up of pulmonary diseases. In this work a new method of non‐contrast‐enhanced functional lung MRI (not dependent on intravenous or inhalative contrast agents) is proposed. A two‐dimensional (2D) true fast imaging with steady precession (TrueFISP) pulse sequence (TR/TE = 1.9 ms/0.8 ms, acquisition time [TA] = 112 ms/image) was implemented on a 1.5T whole‐body MR scanner. The imaging protocol comprised sets of 198 lung images acquired with an imaging rate of 3.33 images/s in coronal and sagittal view. No electrocardiogram (ECG) or respiratory triggering was used. A nonrigid image registration algorithm was applied to compensate for respiratory motion. Rapid data acquisition allowed observing intensity changes in corresponding lung areas with respect to the cardiac and respiratory frequencies. After a Fourier analysis along the time domain, two spectral lines corresponding to both frequencies were used to calculate the perfusion‐ and ventilation‐weighted images. The described method was applied in preliminary studies on volunteers and patients showing clinical relevance to obtain non‐contrast‐enhanced perfusion and ventilation data. Magn Reson Med, 2009.

Regularizing Flows for Constrained Matrix-Valued Images

Variational energy minimization techniques for surface reconstruction are implemented by evolving an active surface according to the solutions of a sequence of elliptic partial differential equations (PDEs). For these techniques, most current approaches to solving the elliptic PDE are iterative involving the implementation of costly finite element methods (FEM) or finite difference methods (FDM). The heavy computational cost of these methods makes practical application to 3D surface reconstruction burdensome. In this paper, we develop a fast spectral method which is applied to 3D active surface reconstruction of star-shaped surfaces parameterized in polar coordinates. For this parameterization the Euler-Lagrange equation is a Helmholtz-type PDE governing a diffusion on the unit sphere. After linearization, we implement a spectral non-iterative solution of the Helmholtz equation by representing the active surface as a double Fourier series over angles in spherical coordinates. We show how this approach can be extended to include region-based penalization. A number of 3D examples and simulation results are presented to illustrate the performance of our fast spectral active surface algorithms.Nonlinear diffusion equations are now widely used to restore and enhance images. They allow to eliminate noise and artifacts while preserving large global features, such as object contours. In this context, we propose a differential-geometric framework to define PDEs acting on some manifold constrained datasets. We consider the case of images taking value into matrix manifolds defined by orthogonal and spectral constraints. We directly incorporate the geometry and natural metric of the underlying configuration space (viewed as a Lie group or a homogeneous space) in the design of the corresponding flows. Our numerical implementation relies on structure-preserving integrators that respect intrinsically the constraints geometry. The efficiency and versatility of this approach are illustrated through the anisotropic smoothing of diffusion tensor volumes in medical imaging.

High spatial and temporal resolution cardiac cine MRI from retrospective reconstruction of data acquired in real time using motion correction and resorting.

Cine MRI is used for assessing cardiac function and flow and is typically based on a breath‐held, segmented data acquisition. Breath holding is particularly difficult for patients with congestive heart failure or in pediatric cases. Real‐time imaging may be used without breath holding or ECG triggering. However, despite the use of rapid imaging sequences and accelerated parallel imaging, real‐time imaging typically has compromised spatial and temporal resolution compared with gated, segmented breath‐held studies. A new method is proposed that produces a cardiac cine across the full cycle, with both high spatial and temporal resolution from a retrospective reconstruction of data acquired over multiple heartbeats during free breathing. The proposed method was compared with conventional cine images in 10 subjects. The resultant image quality for the proposed method (4.2 ± 0.4) without breath holding or gating was comparable to the conventional cine (4.4 ± 0.5) on a five‐point scale (P = n.s.). Motion‐corrected averaging of real‐time acquired cardiac images provides a means of attaining high‐quality cine images with many of the benefits of real‐time imaging, such as free‐breathing acquisition and tolerance to arrhythmias. Magn Reson Med, 2009.

Fully Automatic, Retrospective Enhancement of Real- Time Acquired Cardiac Cine MR Images Using Image- Based Navigators and Respiratory Motion-Corrected Averaging

Real‐time imaging may be clinically important in patients with congestive heart failure, arrhythmias, or in pediatric cases. However, real‐time imaging typically has compromised spatial and temporal resolution compared with gated, segmented studies. To combine the best features of both types of imaging, a new method is proposed that uses parallel imaging to improve temporal resolution of real‐time acquired images at the expense of signal‐to‐noise ratio (SNR), but then produces an SNR‐enhanced cine by means of respiratory motion‐corrected averaging of images acquired in real‐time over multiple heartbeats while free‐breathing. The retrospective processing based on image‐based navigators and nonrigid image registration is fully automated. The proposed method was compared with conventional cine images in 21 subjects. The resultant image quality for the proposed method (3.9 ± 0.44) was comparable to the conventional cine (4.2 ± 0.99) on a 5‐point scale (P = not significant [n.s.]). The conventional method exhibited degraded image quality in cases of arrhythmias whereas the proposed method had uniformly good quality. Motion‐corrected averaging of real‐time acquired cardiac images provides a means of attaining high‐quality cine images with many of the benefits of real‐time imaging, such as free‐breathing acquisition and tolerance to arrhythmias. Magn Reson Med, 2007.

Magnetic resonance lung function – a breakthrough for lung imaging and functional assessment? A phantom study and clinical trial

BackgroundChronic lung diseases are a major issue in public health. A serial pulmonary assessment using imaging techniques free of ionizing radiation and which provides early information on local function impairment would therefore be a considerably important development. Magnetic resonance imaging (MRI) is a powerful tool for the static and dynamic imaging of many organs. Its application in lung imaging however, has been limited due to the low water content of the lung and the artefacts evident at air-tissue interfaces. Many attempts have been made to visualize local ventilation using the inhalation of hyperpolarized gases or gadolinium aerosol responding to MRI. None of these methods are applicable for broad clinical use as they require specific equipment.MethodsWe have shown previously that low-field MRI can be used for static imaging of the lung. Here we show that mathematical processing of data derived from serial MRI scans during the respiratory cycle produces good quality images of local ventilation without any contrast agent. A phantom study and investigations in 85 patients were performed.ResultsThe phantom study proved our theoretical considerations. In 99 patient investigations good correlation (r = 0.8; p ≤ 0.001) was seen for pulmonary function tests and MR ventilation measurements. Small ventilation defects were visualized.ConclusionWith this method, ventilation defects can be diagnosed long before any imaging or pulmonary function test will indicate disease. This surprisingly simple approach could easily be incorporated in clinical routine and may be a breakthrough for lung imaging and functional assessment.

A Review of Recent Advances in Registration Techniques Applied to Minimally Invasive Therapy

Minimally invasive and less invasive procedure is becoming more and more common in medical therapy. Image guidance is an indispensable component in minimally invasive procedures by providing critical information about the position of the target sites and the optimal manipulation of the devices, while the field of view is limited to naked eyes due to the small incision. Registration is one of the enabling technologies for computer-aided image guidance, which brings high-resolution pre-operative data into the operating room to provide more realistic information about the patients anatomy. In this paper, we survey the recent advances in registration techniques applied to minimally and/or less invasive therapy, including a wide variety of therapies in surgery, endoscopy, interventional cardiology, interventional radiology, and hybrid procedures. The registration approaches are categorized into several groups, including projection-to-volume, slice-to-volume, video-to-volume, and volume-to-volume registration. The focus is on recent advances in registration techniques that are specifically developed for minimally and/or less invasive procedures in the following medical specialties: neuroradiology and neurosurgery, cardiac applications, and thoracic-abdominal interventions.

Unsupervised Inline Analysis of Cardiac Perfusion MRI

In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. The entire processing chain has been implemented on clinical MR systems to achieve unsupervised inline analysis of perfusion MRI. Validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach.

Characterization of internal organ motion using skin marker positions

Internal organ motion due to breathing is a phenomenon that nullifies the rigidity assumptions in many interventional applications, ranging from image guided needle biopsies to external beam radiation therapy. In this paper, we propose a method to correlate and characterize internal organ motion with the location of skin markers. The method utilizes a MR time sequence along with tracked magnetic marker positions to establish the correlation. We perform a validation study to quantify the degree of the accuracy and the reproducibility of this correlation. The results demonstrate that patient specific correlation of internal motion and skin markers can be established and the target positioning accuracy of better than 15% of the maximum range of the target movement can be achieved.

Efficient symmetric and inverse-consistent deformable registration through interleaved optimization

Symmetry and inverse consistency are two important features for deformable image registration in medical imaging analysis. This work presents a novel registration method computing symmetric and inverse-consistent image alignment efficiently while preserving high accuracy and consistency of the mapping. This is achieved by optimizing a symmetric energy functional estimating forward and backward transformations constrained by the transformations being inverse to each other. In other words, this approach uses an interleaved optimization scheme borrowed from multiobjective optimization theory constrained by an inverse-consistency criterium. The new optimization scheme provides an efficient search in the space of diffeomorphisms while solving the symmetric registration problem. Moreover, it is not bound to any specific optimizer or energy functional other than to the requirement of being well-defined. In our experiments on clinical cardiac data, superior performance compared to standard, one-directional registration is achieved. The resulting inverse-consistency and symmetry errors match previously reported values while being computed more efficiently. This general approach addresses a clinical need for consistent, highly accurate image alignment achieved in a practically accepted time-frame.

Region-Based Segmentation via Non-Rigid Template Matching

We propose a new region segmentation method based on non-rigid template matching. We align a binary template to an image by maximizing the likelihood of intensity distributions within a region of interest and its background. The intensity model and the corresponding a posteriori distributions are estimated and updated throughout the alignment. The geometric deformation of the template is based on a fluid registration model. Unlike contour-based segmentation techniques, this registration framework allows for a global regularization of the template variations. This enables the segmentation of irregular shapes while avoiding leaks. We apply our method to the segmentation of the liver in computed tomography images, a challenging task due to the high inter-patient variability in the shape of this organ. We show that our segmentation results are equivalent or superior in accuracy to results obtained using existing techniques based on 3D shape models.